System tools for integrating individual load forecasts into a composite load forecast to present a comprehensive, synchronized and harmonized load forecast

ABSTRACT

A system tool merges different load forecasts for power grid centers. A plurality of load forecast engines are coupled to a load forecast interface and a relational data base that saves load forecast engine data as an input through the load forecast interface. A comprehensive operating plan is coupled to the load forecast engines and the relational database. The comprehensive operating plan is configured to integrate individual load forecasts into a composite load forecast to present a comprehensive, synchronized and harmonized load forecast. A program interface provides access to the composite load forecasting schedule.

RELATED APPLICATION

The present application is a continuation of, and claims priority to,U.S. patent application Ser. No. 12/830,038, filed Jul. 2, 2010, andentitled “SYSTEM TOOLS FOR INTEGRATING INDIVIDUAL LOAD FORECASTS INTO ACOMPOSITE LOAD FORECAST TO PRESENT A COMPREHENSIVE, SYNCHRONIZED ANDHARMONIZED LOAD FORECAST,” the entirety of which is hereby incorporatedby reference herein.

TECHNICAL FIELD

The present invention relates generally to load forecast and moreparticularly to system tools for integrating individual load forecastsinto a composite load forecast to present a comprehensive, synchronizedand harmonized load forecast.

BACKGROUND

With respect to the electric power grid, expensive peak power—electricpower delivered during periods of peak demand—can cost substantiallymore than off-peak power. The electric power grid itself has becomeincreasingly unreliable and antiquated, as evidenced by frequentlarge-scale power outages. Grid instability wastes energy, both directlyand indirectly, for example, by encouraging power consumers to installinefficient forms of backup generation.

Clean forms of energy generation, such as wind and solar, suffer fromintermittency. Hence, grid operators are reluctant to rely heavily onthese sources, making it difficult to move away from standard, typicallycarbon-intensive forms of electricity.

The electric power grid contains limited inherent facility for storingelectrical energy. Electricity must be generated in a balanced fashionto meet uncertain demand, which often results in either over or undercommitment or dispatch of generation, hence system inefficiency, systeminsecurity and power failures.

Distributed electric resources, en masse can provide a significantresource for addressing the above problems. However, current powerservices infrastructure lacks provisioning and flexibility that arerequired for aggregating a large number of smallscale resources (e.g.,electric vehicle batteries) to meet medium- and large-scale needs ofpower services.

Classical dispatch of energy, (i) is cost based with centralizedgeneration, (ii) is passive with static demand, (iii) has inaccurateparameters, (iv) has manual re-dispatch to relieve grid securityviolations, (v) uses ad-hoc forward scheduling that is disconnected fromreal time dispatch, (vi) designed only for rather normallyinter-connected system operation and (vii) is limited in forensicanalysis.

Thus, significant opportunities for improvement exist in the electricalsector. Real-time balancing of generation and load can be realized withreduced cost and environmental impact. More economical, reliableelectrical power can be provided at times of peak demand. Powerservices, such as regulation and spinning reserves, can be provided toelectricity markets to stabilize the grid and provide a significanteconomic opportunity. Technologies can be enabled to provide broader useof intermittent power sources, such as wind and solar.

There is a need for methods and system tools that merge different loadforecasts for power grid centers. There is a further need for methodsand system tools that integrate individual load forecasts are into acomposite load forecast to present a comprehensive, synchronized andharmonized load forecast. There is a further need for methods and systemtools to merge different load forecasts for power grid centers thatallows an operator to review input load forecasts and modify loadforecasts with an override capability. There is another need for needfor methods and system tools that merge different load forecasts forpower grid centers and improved load forecasts are provided overdifferent time horizons.

SUMMARY

An object of the present invention is to provide system tools that mergedifferent load forecasts for power grid centers.

Another object of the present invention is to provide system tools thatintegrate individual load forecasts are into a composite load forecastto present a comprehensive, synchronized and harmonized load forecast.

A further object of the present invention is to provide system tools tomerge different load forecasts for power grid centers that allow anoperator to review input load forecasts and modify load forecasts withan override capability.

Yet another object of the present invention is to provide system toolsthat merge different load forecasts for power grid centers and improvedload forecasts are provided over different time horizons.

Still another object of the present invention is to provide system toolsthat merge different load forecasts for power grid centers where loadforecast requests from different clients are served with consistent loadforecast data.

Another object of the present invention is to provide system tools thatmerge different load forecasts for power grid centers that has anoperator interface to a plurality of load forecast engines andconfigured to streamline with a reduction of repetitive manual entries.

Yet another object of the present invention is to provide system toolsthat merge different load forecasts for power grid centers that permitsan operator to view individual load forecasts and a composite loadforecast and perform an operator override selected from at least one of,scaling, adding, setting and linear transformation.

A further object of the present invention is to provide system toolsthat merge different load forecasts for power grid centers thatsynthesizes a composite load profile for any given scheduling studyperiod.

Still another object of the present invention is to provide system toolsthat merge different load forecasts for power grid centers with boundaryprocessing that includes at least one of priority and averaging.

In one embodiment of the present invention, a system tool is providedthat merges different load forecasts for power grid centers. A pluralityof load forecast engines are coupled to a load forecast interface and arelational data base that saves load forecast engine data as an inputthrough the load forecast interface. A comprehensive operating plan iscoupled to the load forecast engines and the relational database. Thecomprehensive operating plan is configured to integrate individual loadforecasts into a composite load forecast to present a comprehensive,synchronized and harmonized load forecast. A program interface providesaccess to the composite load forecasting schedule.

In another embodiment of the present invention, a system tool mergesdifferent load forecasts for power grid centers. A plurality of loadforecast engines are provided within a common service framework and arecoupled to a load forecast interface. A relational data base saves loadforecast engine data as an input thru the load forecast interface. Acomprehensive operating plan is coupled to the load forecast engines andthe relational database and configured to integrate individual loadforecasts into a composite load forecast to present a comprehensive,synchronized and harmonized load forecast.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a dispatch system of the presentinvention illustrating time and scenario dimensions.

FIG. 2 illustrates one embodiment of the present invention with dayahead generation scheduling with coordination via plans.

FIG. 3 illustrates one embodiment of an energy system tool fordispatchers in power grid control systems of the present inventionillustrating the coordination of multi-stage resource schedulingdispatch and commitment function engines, hereafter, SKED engines with acomprehensive operating plan (COP).

FIG. 4 illustrates one embodiment of the present invention with amulti-stage SKED overview for multi-stage scheduling processes.

FIG. 5 illustrates one embodiment of a comprehensive operating plan usedwith the present invention.

FIG. 6 illustrates the coordination between a SKED engine and thecomprehensive operating plan.

FIG. 7 illustrates one embodiment of a control center and generationscheduling coordination.

FIG. 8 illustrates one embodiment of SKED2 and SKED3 coordination.

FIG. 9 is a graph that illustrates a SKED execution timeline.

FIG. 10 illustrates one embodiment of a component architecture and dataflow for a demand forecast integrator of the present invention.

FIG. 11 illustrates one embodiment of a load forecast area tree for ademand forecast integrator.

FIG. 12 is a graph that illustrates load forecast construction for asingle load forecast area for a demand forecast integrator.

FIG. 13 is a graph illustrating load forecast source weighting factorsthat can be used with a demand forecast integrator of the presentinvention.

FIG. 14 graphically illustrates a use case for load forecast overrideconsiderations for a demand forecast integrator in one embodiment of thepresent invention.

FIG. 15 illustrates one embodiment of a demand forecast integratorsoftware architecture that can be used with the present invention.

FIG. 16 illustrates an example of structure of an Internal service layerof a demand forecast integrator in one embodiment of the presentinvention.

FIG. 17 illustrates one embodiment of a demand forecast integratorphysical data layer that can be used with the present invention.

FIG. 18 illustrates a functional block diagram of an after the faceanalysis tool (AFA) of the present invention.

FIG. 19 illustrates one embodiment of perfect dispatch that can be usedwith AFA in one embodiment of the present invention.

FIG. 20 illustrates one embodiment of new schema for historical datathat can be used with AFA in one embodiment of the present invention.

FIG. 21 illustrates the use of impact factors in one embodiment of AFAof the present invention.

FIG. 22 illustrates a read only display of an embodiment of AFA caseactual values and displays actual values for each AFA case.

FIG. 23 illustrates an impact factor input display that supportsinserting, updating and deleting impact factor delta values in AFA inone embodiment of the present invention.

FIG. 24 illustrates a list of read only summary displays for marketoperator interface (MOI) solutions in one embodiment of the presentinvention.

FIG. 25 illustrates a saving summary display to show top ten savingunits and saving by types in one embodiment of the present invention.

FIG. 26 illustrates an embodiment of the present invention of casecomparison.

FIG. 27 illustrates another embodiment of the present invention of casecomparison.

FIG. 28 illustrates an embodiment of the present invention creating andediting mappings.

FIG. 29 illustrates an embodiment of the present invention for creatingand editing mappings with setting query procedure parameters stored in adatabase.

FIGS. 30 and 31 one embodiment of the present invention for creating andediting mappings where the last step is saving the mapping or/and runexport.

DETAILED DESCRIPTION

The following terms and acronyms are used in this specification:

CXF—Apache CXF is an open source services framework. CXF helps you buildand develop services using frontend programming APIs,

JAXB—Java Architecture for XML Binding (JAXB) allows Java developers tomap Java classes to XML representations

Hibernate—A powerful, high performance object/relational persistence andquery service,

Spring Framework—container: configuration of application components andlifecycle management of Java objects

WSDL (web services description language) is an XML format for describingnetwork services as a set of endpoints operating on messages containingeither document-oriented or procedure-oriented information.

In one embodiment of the present invention, a system tool mergesdifferent load forecasts for power grid centers. A plurality of loadforecast engines are coupled to a load forecast interface and arelational data base that saves load forecast engine data as an inputthrough the load forecast interface. A comprehensive operating plan iscoupled to the load forecast engines and the relational database. Thecomprehensive operating plan is configured to integrate individual loadforecasts into a composite load forecast to present a comprehensive,synchronized and harmonized load forecast. A program interface providesaccess to the composite load forecasting schedule.

In another embodiment of the present invention, a system tool mergesdifferent load forecasts for power grid centers. A plurality of loadforecast engines are provided within a common service framework and arecoupled to a load forecast interface. A relational data base saves loadforecast engine data as an input thru the load forecast interface. Acomprehensive operating plan is coupled to the load forecast engines andthe relational database and configured to integrate individual loadforecasts into a composite load forecast to present a comprehensive,synchronized and harmonized load forecast.

In one embodiment, the present invention provides an energy system toolfor dispatchers in power grid control systems that is suitable forrenewable resources and the demand response. Outputs of many of therenewable resources do not follow traditional generation/loadcorrelation but have strong dependencies on weather conditions, whichfrom a system prospective are posing new challenges associated with themonitoring and controllability of the demand-supply balance. Asdistributed generations, demand response and renewable energy resourcesbecome significant portions of overall system installed capacity, asmarter system tool for dispatchers in power grid control systems forgeneration resources is required to cope with the new uncertaintiesbeing Introduced by the new resources.

In one embodiment, the energy system tool for dispatchers in power gridcontrol systems of the present invention is provided that copes with theincreasing uncertainties introduced by distributed energy resources andprovides a better holistic and forward-looking view of system conditionsand generation patterns to enable system operators to make betterdecisions. Such features are deemed critical for the success ofefficient power system operations in the near future.

In one embodiment of the present invention, an energy system tool fordispatchers in power grid control systems is provided that is suitablefor uncertainties associated with renewal energy resources and demandresponse in order to create a better predictive model. In oneembodiment, the system tool for dispatchers in power grid controlsystems of the present invention provides improved modeling oftransmission constraints, better modeling of resource characteristicssuch as capacity limits and ramp rates, more accurate demand forecastingand the like. In another embodiment, the energy system tool fordispatchers in power grid control systems of the present inventionaddresses the robustness of dispatch solutions. Optimality or evenfeasibility of dispatch solutions can be very sensitive to systemuncertainties.

In one embodiment of the present invention, a method is provided thatenables dispatchers in power grid control centers to manage changes byapplying multi-interval dispatch. A SKED engine, and a comprehensiveoperating plan are used. Multiple system parameter scenarios arecoordinated. Each SKED engine is capable of providing at least one of,scheduling, dispatch and commitment.

In another embodiment of the present invention, a method providesdispatchers in power grid control centers a capability to manage changesusing multi-interval dispatch. A multi-stage resource engine SKED, and acomprehensive operating plan are provided. The SKED engine includes atleast SKED1, SKED2, and SKED3 to address scheduling proposes ofdifferent time frame. Multiple system parameter scenarios arecoordinated.

When compared to the classical dispatch which only deals with aparticular scenario for a single time point, the present inventionaddresses a spectrum of scenarios for a broad time range. Scenarioanalysis assesses the sensitivity or robustness of resourcecommitment/dispatch solutions with respect to scenario perturbation.However, the expansion in time and scenarios for the present inventionmakes the problem of the present invention itself fairly challengingfrom both a computational perspective and a user interface presentationperspective. Not only the general control application and after the factanalysis need to handle multiple time interval analysis which iscomputationally more demanding, but also there is significant challengeto effectively present multi-dimensional resultant data to systemoperators without overwhelming them. FIG. 1 schematically describes howapplications of the system tool for dispatchers in power grid controlsystems of the present invention span the space of time and scenarios.

In one embodiment, the present invention provides an energy system toolfor dispatchers in power grid control systems that is suitable forrenewable resources and the demand response. Outputs of many of therenewable resources do not follow traditional generation/loadcorrelation but have strong dependencies on weather conditions, whichfrom a system prospective are posing new challenges associated with themonitoring and controllability of the demand-supply balance. Asdistributed generations, demand response and renewable energy resourcesbecome significant portions of overall system installed capacity, asmarter system tool for dispatchers in power grid control systems forgeneration resources is required to cope with the new uncertaintiesbeing Introduced by the new resources.

In one embodiment, the energy system tool for dispatchers in power gridcontrol systems of the present invention is provided that copes with theincreasing uncertainties introduced by distributed energy resources andprovides a better holistic and forward-looking view of system conditionsand generation patterns to enable system operators to make betterdecisions. Such features are deemed critical for the success ofefficient power system operations in the near future.

In one embodiment of the present invention, an energy system tool fordispatchers in power grid control systems is provided that is suitablefor uncertainties associated with renewal energy resources and demandresponse in order to create a better predictive model. In oneembodiment, the system tool for dispatchers in power grid controlsystems of the present invention provides improved modeling oftransmission constraints, better modeling of resource characteristicssuch as capacity limits and ramp rates, more accurate demand forecastingand the like. In another embodiment, the energy system tool fordispatchers in power grid control systems of the present inventionaddresses the robustness of dispatch solutions. Optimality or evenfeasibility of dispatch solutions can be very sensitive to systemuncertainties.

The system tool for dispatchers in power grid control systems of thepresent invention can provide one or more of the following, (i)extension for price-based, distributed, less predictable resources, (ii)active, dynamic demand, (iii) modeling parameter adaptation, (iv)congestion management with security constrained optimization, (v)continuum from forward scheduling to real-time dispatch, (vi) extensionfor dynamic, multi-island operation in emergency and restoration, (vii)after-the-fact analysis for performance matrices, root-cause impacts andprocess re-engineering and the like.

In one embodiment, the system tool of the present invention providesmulti-stage engines, including but not limited to SKED 1,2 and 3engines, the comprehensive operating plan, adaptive model management andthe like. Each SKED engine performs at least of, scheduling, commitmentof resources, and dispatch of resources, depending on the application.Each SKED engine can be a mixed integer programming/linear programmingbased optimization application which includes both unit commitment andunit dispatch functions. Unit commitment is the process of preparing aunit to generate at some point in the future usually taken intoconsideration of the various unit characteristics including time andcost factors.

Each SKED engine can be easily configured to perform schedulingprocesses with different heart beats and different look-ahead time. Themulti-stage resource scheduling, SKED engine, process is securityconstrained unit commitment and economic dispatch sequences withdifferent look-ahead periods, which as a non-limiting example can be 6hours, 2 hours and 20 minutes, updating resource schedules at differentcycle frequencies (e.g. 5 min, 15 min or hourly). The results of eachstage form progressively refined regions that guide the dispatchingdecision space of the subsequent stages. Various SKED engine cycles arecoordinated through a comprehensive operating plan, as more fullyexplained hereafter.

In one embodiment, the present invention provides a general controlapplication which enhances an operators' decision making process underchanging system conditions, including but not limited to, load,generation, interchanges, transmission constraints and the like, in nearreal-time. The general control application to enhance an operators'decision making process under changing system condition, load,generation, interchanges, transmission constraints, the like, is in nearreal-time or at real time. The general control application is composedof several distinct elements.

The general control application is an application designed to providedispatchers in large power grid control centers with the capability tomanage changes in load, generation, interchange and transmissionsecurity constraints simultaneously on a intra-day and near real-timeoperational basis. The general control application uses least-costsecurity-constrained economic scheduling and dispatch algorithms withresource commitment capability to perform analysis of the desiredgeneration dispatch. With the latest state estimation solution as thestarting point and transmission constraint data from the energymanagement system, general control application optimization engines, theSKED engine, will look ahead at different time frames to forecast systemconditions and alter generation patterns within those timeframes. TheSKED engines coordinate with the comprehensive operating plan. Aconstraint means one or more resources called on by the operator formanaging the congestion that occurs with a lack or dimension of atransmission ability.

A general control application SKED engine function is a series ofmulti-stage scheduling processes. The results of each stage formprogressively refined regions that guide the dispatching contour of thesubsequent stage. By having a time-profiled representation for eachstage, and then systematically linking adjoining stages into a series ofconsistently refined schedules, the general control application providesrealistic dispatch and pricing signals for resources to follow.

In one embodiment, the general control application is built on a modularand flexible system architecture. Different SKED engine processes can becorrelated and need not replay on each other. Orchestration between anindividual engine (SKEDi) can be managed by the comprehensive operatingplan. This embodiment provides low-risk, cost-effective business processevolution. Additionally, this embodiment also ensures high availabilityfor the mission critical real-time general control application SKEDengine functions. Failure of any one or more SKED engine componentscauses smooth degradation of, instead of abrupt service interruptionsto, real-time dispatch instructions.

As a non-limiting example, a configuration for the general controlapplication can include three SKED engine sequences. SKED1 can providethe system operator with intra-day incremental resource, includinggenerators and demand side responses, commitment/de-commitment schedulesbased on day-ahead unit commitment decision to manage forecastedupcoming peak and valley demands and interchange schedules whilesatisfying transmission security constraints and reserve capacityrequirements.

The general control application can be utilized to provide dispatchersin large power grid control centers with the capability to managechanges in load, generation, interchange and transmission securityconstraints simultaneously on a intra-day and near real-time operationalbasis. The general control application uses least-costsecurity-constrained economic scheduling and dispatch algorithms withresource commitment capability to perform analysis of the desiredgeneration dispatch. State estimator is computer software to estimatethe quasi-steady state of the power system. With the latest stateestimator solution as the starting point and transmission constraintdata from the energy management system, general control applicationoptimization engines, a SKED engine, looks ahead at different timeframes to forecast system conditions and alter generation patternswithin those timeframes.

In one embodiment, the key components of general control applicationinclude: security constrained economic dispatch—SKED engines 1, 2, 3 andorchestrator and memory. In one embodiment, three SKED engine sequenceshave different business objectives and different look-ahead periods.SKED1 is envisioned to perform the “day of” commitment akin to the dayahead market/reliability commitment function, taking into accountsomewhat real-time and projected system conditions. SKED 2 will positionthe steam units at the appropriate time to allow for various other units(combustion turbine's and hydro) to startup or shutdown. Guided by SKED2, SKED 3 provides more accurate unit dispatch trajectory for next, as anon-limiting example 15-20 minutes, with more accurate forecastinginputs.

The orchestrator and memory can be envisioned as an intelligent dispatchmanagement system. In one embodiment, orchestrator and memory permitsthe general control application to be both scalable and flexible, andadaptable to business rule changes and/or creation of new componentswithin the general control application (“plug-n-play”). The orchestratorand memory component can take information from all general controlapplication components and provide cohesive dispatch solutions allowingfor operational decisions to be both timely and cost-effective.

In one embodiment, the general control application provides day-aheadgeneration scheduling with coordination via plans, as illustrated inFIG. 2. In one embodiment, the general control application product ofthe present invention includes: a market clearing engine which combinesboth unit commitment and unit dispatch, that can be configured as SKED1,2, 3, . . . , I, a comprehensive operating plan which coordinatesinput/solution between different SKEDs and allow easy implementation ofa future SKEDi, a NET based market operator interface that supportsoperators to obtain system situation awareness and to make properdecisions and the like. The market operator interface is the userinterface for the market operators to operate the electricity market.

In one embodiment, the performance requirement from the general controlapplication comes from real-time or look-ahead business process needs,illustrated in FIG. 3. As a non-limiting example, at the minimum, SKED2run time will not exceed 5 minutes with a typical 2-hour SKED2 caseduration which has 15-minute intervals for the first hour and 30-minuteintervals for the second hour.

In one embodiment, the business requirements for the general controlapplication, include a resource model for supporting existing resourcemodel, including price based model and fuel model.

As a non-limiting example, model resource block loading can include oneor more of, (i) model combined cycle units and pumped storage units inthe same way as they are modeled in the existing system. For combinedcycle units, all the three existing models, including steam factor basedmodel, composite model and component unit model can be supported. PumpedStorage units can be modeled as fixed schedules, (ii) Support hydroresource schedules on the plant and individual resource level, (iii)Support megawatt dependent directional (Up and Down) ramp-rates. For theeach interval the average ramp rates are calculated based on the currentstate estimator generation level and the maximum/minimum levelachievable in the corresponding interval duration, (iv) demand sideresource with shutdown cost and multi-segment energy bid curves, (v)resource Static/daily/Interval model parameters, order of precedence,time period of validity, eligibility to participate in differentancillary services products, (vii) resource control modes: integrationof physical model (e.g. notification time) and User actions (e.g.startup/shutdown acknowledgements), (viii) Support multiple sets ofinterval based resource operating limits (potentially with differentpenalty costs), e.g., economic high/low limits, regulation high/lowlimits, emergency high/low limits envelope high/low limits (Envelopelimits are calculated based on the resource's megawatt dispatch for thelast interval in the latest COPt), (ix) Support interval based virtualinterchange transaction models, (x) Support emission model as objectiveor constraints, and the like.

Model energy transactions can be as a fixed megawatt schedule ordispatchable transactions. Transmission constraint impact of incrementalchanges in transaction dispatches with respect to the current real timetransaction schedules can be modeled as incremental injections at theinterface nodes.

Model energy transactions can be as individual transactions or asforecasted net interchanges between control center of a power gridcontrol system, and other control areas. With user configurability,transmission constraint impact of net interchange transaction schedulescan be modeled by way of example, (i) via appropriate scaling ofexternal generations; (ii) by applying incremental injection changes atthe corresponding interface nodes, and the like

In one embodiment, transmission losses are modeled. There can be somemodel transmission security constraints, including but not limited to,alleviate overload constraints, simultaneous feasibility testconstraints and watch list constraints. Constraint the right hand sideand penalty cost for alleviate overload constraints can be intervaldependent. Constraint the right hand side bias can be calculated forwatchlist alleviate overload constraints. Multiple segment can bemodeled based penalty functions. Activation of any transmissionconstraints can be controlled by the operator. Transmission gridtopology can come from state estimator, planned outages, unplannedoutages and the like.

In various embodiments, the comprehensive operating plan Includes acentral repository of various scheduling data to and from a certainclass of power system applications. The comprehensive operating planpresents a comprehensive, synchronized and more harmonized view ofscheduling data to various applications related to power systemoperations. The class of scheduling data of interest can include thefollowing: (1) a resource megawatt schedule; (2) a demand forecast; (3)an outage schedule; (4) a transaction and Interchange schedule; (5) atransmission constraint limit schedule; (6) a reserve and regulationrequirement schedule; (7) a resource characteristics schedule, and thelike.

The comprehensive operating plan is the repository of all operatingplans in a multi-stage decision process. Each SKEDi in the decisionprocess generates a set of schedules that are reflected in itscorresponding initial (COPi). The aggregated results from themulti-stage decision process are captured in the total comprehensiveoperating plan (COPt), which is the consolidated outcome of theindividual COPi's. SKED engines and the comprehensive operating plancoordination are illustrated in FIG. 3.

In one embodiment, the system tool for dispatchers in power grid controlsystems of the present invention can include, individual schedulingapplication engines: input, output and internal optimization processing,and the like. Data structures hold the comprehensive operating plans,including but not limited to resource plans, and the interactionsbetween comprehensive operating plans and SKED engine applications areillustrated in FIG. 4. The FIG. 4 embodiment includes the followingbenefits: (1) Modular SKED: SKEDi can be coordinated using subordinateCOPi's which can be synchronized into the overall COPt. This removes theneed for individual SKED engine applications to communicate with eachother directly. (2) SKED's may be added, removed and/or modified withminimal impact on the other SKEDs and comprehensive operating plans.This intrinsic flexibility enables low-risk, cost-effective businessprocess evolution. It also ensures high availability for the missioncritical real-time general control application SKED engine functions.Failure of any one or more SKED engine components will cause smoothdegradation of, instead of abrupt service interruptions to, real-timedispatch Instructions. The comprehensive operating plan input to eachSKEDi comes from the overall COPt, which is the level that operatorsinteract with. All operator overrides to the comprehensive operatingplan can be captured and included as input to SKEDi's.

The comprehensive operating plan contains quantities, as a non-limitingexample megawatt generation level, being scheduled over differentoperating Intervals. Operator interaction can be typically with COPt. Inone embodiment, initialization of the comprehensive operating plan foreach operating day begins with the day-ahead schedule, which is based onthe day ahead marketing financial schedules and then updated withreliability commitment results. Before any SKEDi is run in the currentday of operation, the overall COPt is initialized with the day-aheadschedules. When COPt is suitably initialized, it can be used to generateinput data for SKED1, SKED2 and SKED3. Results of SKEDi's are then usedto update their respective subordinate COPi, which will cause COPt to beupdated, and thus the overall iterative process continues.

In one embodiment of the present invention, the comprehensive operatingplan requirements can be as follows: (1) Each SKED engine has its owncomprehensive operating plan; COPi will only be populated by approvedSKEDi solution; (2) COPt can be a comprehensive plan to support decisionmaking by operators; (3) COPt can be Initialized from approved day-aheadschedule; (4) Information content in COPt can be displayed in acomprehensive operating plan user interface at least one or more of,resource, resource type, control zone/area, company, and system level;5) COPt exports input data for SKEDi, however, SKEDi can survive withmissing/incomplete COPt data (e.g. undefined commitment in thecomprehensive operating plan), the latest available COPt can be alwaysused for the SKED2 runs and (6) Inconsistencies between COPi and COPtare identified and resolved.

Any energy (megawatt hour) quantity in the comprehensive operating planis understood to be expressed in terms of a constant power megawattlevel over the duration of the scheduling interval. As a non-limitingexample, the scheduling intervals can be, 100 megawatts×15 minutes canbe shown the quantity 100 instead of 25, even though the actual energyis 25 mwh.

The comprehensive operating plan can contain comprehensive summaryinformation. Summary information can be rollups from a raw data at alower level, including but not limited to resource level, according tosome pre-defined system structures.

The comprehensive operating plan can have a presentation layer or a setof operator user interfaces to support system operator decision making.However, it does not intend to replace but supplement user interfaces ofindividual scheduling applications. The comprehensive operating plan canhave a service layer to facilitate the interaction with external powersystem applications or data sources. The comprehensive operating planalso can have a business application layer that performs validation,translation, transformation, consolidation and harmonization of variousasynchronized scheduling data. Lastly, the comprehensive operating plancan have a persistence data layer for storing key scheduling datarelated to power system operations as Illustrated in FIG. 5.

The comprehensive operating plan contains individual resource level andcomprehensive summary information. A summary can be rollups from theresource level or stored in a database. The comprehensive operating plancan also Include system level information including but not limited to ademand forecast, reserve requirements and the like. Contents of COPi donot have to be identical but must allow for explicittranslation/transformation between each COPi and COPt. As a non-limitingexample, the schedule time-line can be as follows: variable timeduration (minimum 5 minutes, typical 5 to 60 minutes); monotonicallynon-decreasing; each n+1 interval is an integer multiple of the previousinterval n. (e.g. 5, 5, 5, 5, 15, 15, 30, 30 . . . ). Each Interval isassociated with an absolute start/end time and supports DST and multipletime zones.

COPt contains the current operating plan used to guide SKED enginesolutions. It can be initialized with the results from the day ahead andreliability cases and then updated based upon accepted SKED enginesolutions. Dispatchers may override values stored in COPt.

FIG. 6 illustrates the coordination between SKED engines and thecomprehensive operating plan.

For the comprehensive operating plan initialization, the data for COPtcan be populated in a sequential process from an hourly resolution forday-ahead results up to the 5 minute intervals from a SKED3 execution.The sequence for COPt initialization can be as follows. COPt can beinitialized from day ahead resulting in hourly intervals to beestablished for the next operating day. Reliability commitment resultsand other real-time manual commitment decisions may be used to updatethe COPt via dispatcher manual entries via some market operator's userinterface.

SKED2 executes and solves multiple scenarios. The dispatcher reviews theresults and may accept the solution for a scenario. The acceptanceprocess will populate the COP2 and COPt values based on the selectedscenario. A scenario may be accepted as long as it does not contain anyintervals that are in the past. COPt can be updated for the intervalsthat have not already been updated based on a SKED3 solution. The COPtintervals can transition, as a non-limiting example, from hourly to the15 and 30 minute interval duration of the SKED2 solution.

SKED3 executes in automatic mode and if the solution passes thevalidation check it can be automatically accepted and used to populatethe COP3 values as well as the values in COPt. The COPt intervals willtransition to the 5 minute interval duration of the SKED3 solution.

The comprehensive operating plan can be used for SKEDi execution. COPtcan be the source of all operating plan data for every execution ofSKED.

Manual overrides can be applied against COPt. Overrides can be capturedand maintained such that if they are removed, COPt will revert to thelast value set by a SKED engine execution. Operators can be responsibleto ensure validity of the overrides.

In general, limited validation can be performed during the overrideprocess; to verify the impacts of the overrides on the resultantcomprehensive operating plan, it would be necessary to re-executeappropriate SKED's. Impact of operator overrides on resource modes,which are modes for operations for generating resources or demandresources, which: an operator may directly specify resource modes (byresource/product/time intervals). An operator may not override dispatchmegawatt base points of a unit.

With reference to resource modes, for each resource in each schedulinginterval, resource modes can be used to specify whether current COPtquantity, such as megawatt dispatch, commitment status, ancillaryservices contributions, and the like, can be fixed or adjustable, orused with appropriate level of inertia. Resource modes can be determinedbased upon a combination of unit modeling parameters, including but notlimited to notification time, business rules, as a non-limiting exampleno steam unit commitment change in SKED2, current unit status such as acombustion turbine has been called, operator overrides, and the like.

Resource modes may be time-varying, where the time is measured relativeto the current time, which as a non-limiting example can be a fixedcommitment within 2 hours of real-time, flexible commit beyond twohours. For the given SKED engine the following can be provided; (1) theunits may have different modes for different target intervals; (2)boundary/envelope determination; (3) megawatt dispatch range at thefinal interval of SKEDi; (4) energy consumption reserved for laterintervals; (5) maximum number of available starts/stops taking intoaccount reservations for future intervals; (6) an operating band forenergy schedule, for each interval; (7) operating band for regulationschedule for each interval, and the like.

A case solution of SKEDi can be uploaded to the comprehensive operatingplan. Support for this case solution of SKEDi can include the following:highlight solutions summaries; Identification of important changes tothe COPt due to this SKEDi solution; comments on case solution (such aswhy a particular case is accepted or not); special concerns with thecase that may require operator overrides later, and the like;identification of important changes in Inputs; accepted case solutioncan be uploaded to COPi in its entirety, such as in one embodiment thereis no partial case loading in general and no override on case solutions;and the like. If there are fields in COPi that are not explicitlycontained in the case solution files, then the Importer will need tofurnish the information, assuming the information can be assembled fromthe case solution data.

COPt can be updated when a new COPi is accepted. Accepted solutionsupdate the data set of matching intervals in COPt as long as thesolutions are for the same SKED, or for a higher SKED engine (e.g. SKED3replaces SKED2 but SKED2 will not replace SKED3). COPt has,conceptually, several layers where each layer can be associated with aCOPt. Each data item in COPt has a source COPt. Each COPi can beassigned a priority which determines if it can replace an existingsource for COPt.

COPt can be updated with dispatch management tool. No matter where thedecision originates including but not limited to intra-Day reliabilityassessment, SKED1, ancillary services market clearing and the like.Ancillary services are those functions performed by electricalgenerating, transmission, system-control, and distribution systemequipment and people to support the basic services of generatingcapacity, energy supply, and power delivery. The electricity market tosupport the procurement of such ancillary services is an ancillaryservices market.

A dispatch management tool is a place for the operator to call on/off aunit, assuming that all calls via phone are captured by dispatchmanagement tool. A dispatch management tool entry can automaticallytrigger the update of COPt in accordance with the notification time, minup/down time of a unit.

COPt can be updated with a state estimator. As a non-limiting example,resource status and the minimum and maximum megawatt can be captured bya 5 minute interval in COPt. The comprehensive operating plan data canbe saved for each SKED engine (e.g. SKED2, SKED3) as well as a finalcoordinated set (COPt). In one embodiment, dispatcher overrides of thecomprehensive operating plan can only be allowed on COPt.

As a non-limiting example, the comprehensive operating plan informationcan be available with variable intervals, as a non-limiting example, 5,15, 30, 60 and the like. Comprehensive operating plan information caninclude but not limited to the following items: (1) interval specific;qualification times; (2) the regional transmission organization loadforecast; (3) the regional transmission organization Interchange; (4)bias megawatt; (5) caseID; (6) operating condition including but notlimited to emergency, spin event, and the like; (6) number of resourcescommitted and de-committed; (7) resource specific data; (8) dispatchmegawatt; (9) limits that include economic, envelope and the like; (9)ramp rates; (10) commitment status and reason codes; (11) reserveassignment; (12) regulation assignment; (13) scarcity pricing unit; (15)resource schedule cost or price for the interval; (16) qualifications;(17) initial conditions; (18) locational marginal prices/marginal costs;(19) pre-ramping units; (20) constraint specific data; (21) constrainttype such as simultaneous feasibility test or alleviate overload, flowgate, and the like; (22) monitored or enforced or relaxed; constraintlimits including but not limited to, facility limits, target controllimits and the like; (23) constraint flows such as state estimatorflows, dispatch based flows; (24) marginal values and limits of modelingconstraints; and the like.

The comprehensive operating plan can be used to build a unifiedscheduling framework, which coordinates between a series of resourcescheduling processes in modern grid control centers. The unifiedscheduling framework is a framework to unify different resourcescheduling and commitment functions. These scheduling processes rangefrom annual planning to real-time dispatch. As a non-limiting example, acontrol center of a power grid control system is used to illustrate howdifferent scheduling processes are integrated together via thecomprehensive operating plan.

In one embodiment, a control center of a power grid control system,generation scheduling system can include the following major processes,(i) rolling forward scheduling monthly contract generation based onannual contracts, (ii) rolling forward scheduling daily contractgeneration based on monthly contracts, (iii) day-ahead quarter-hourlygeneration schedule, (iv) rolling forward intra-day the general controlapplication SKEDs and the like

These scheduling processes are can be correlated to each other. FIG. 7demonstrates the main data flow from annual generation scheduling allthe way into day ahead scheduling.

The data communication can be through an extended comprehensiveoperating plan as described in this non-limiting example. In thisnon-limiting example, the process starts with annual generationcontracts signed between control center of a power grid control system,and the generation plants in its footprint, as a non-limiting examplecontracts for 2008. The control center of a power grid control systemdetermines each unit's monthly generation energy target by schedulingthe units to meet their plant annual contracts and satisfyingtransmission security constraints. This process is repeated each monthfor the remaining of the year to refine the monthly energy targets andemission quota, which can be stored in the comprehensive operating plan.

The monthly scheduling process takes monthly energy targets from thecomprehensive operating plan and determine plant daily energy targetwhile respect transmission security. This process repeats each day forthe remaining days in the month in order to refine the plant dailyenergy and emission quota, which can be stored in the comprehensiveoperating plan. The control center of a power grid control system, whichcan have security constrained day-ahead scheduling processes, can usethe daily energy target in the comprehensive operating plan to enforcedaily maximum energy constraint and produce an optimized unit commitmentdecision. The day-ahead commitment decision is stored in thecomprehensive operating plan.

Coming into the operating day, the control center of a power gridcontrol system, runs general control application SKED engines to adjustfast units' commitment and produce unit dispatch based on the day-aheadcommitment from the COP.

The comprehensive operating plan control center of a power grid controlsystem, can thus integrate its multiple generation scheduling processesinto a unified scheduling system, which significantly streamlines thebusiness process and improve the efficiency, as illustrated in FIG. 8.

SKED1 is a mixed integer programming based application. As anon-limiting example, it can be configured to execute for a look-aheadwindow of 6-8 hours with viable interval durations, e.g., 15-minuteintervals for the 1^(st) hour and hourly intervals for the rest of studyperiod.

As a non-limiting example, SKED2 can look 1-2 hour ahead with 15-minuteintervals. Based on the latest system load and reserve requirements,interchange schedules and security constraints, SKED2 will commitadditional qualified fast start resources as needed and fine-tune thecommitment status of qualified fast start resources committed by SKED1.SKED2 produces dispatch contours and provides resource ramping envelopesfor SKED3 to follow. As illustrated in FIG. 8, the envelope ofreachability creates solution coupling effect across schedulingfunctions of SKED2 and SKED3. SKED3 can be a dispatch tool whichcalculates the financially binding base points of the next five-minutedispatch interval and advisory base-points of the next several intervalsfor each resource, as a non-limiting example, 5 min, 10 min, 15 min, andthe like).

SKED3 can also calculate ex-ante real-time locational marginal pricesfor the financial binding interval and advisory price signals for therest of study intervals. SKED3 is a multi-interval co-optimizationlinear programming problem. Therefore, it could pre-ramp a resource forthe need of load following and real-time transmission congestionmanagement.

In one embodiment, a SKED engine formulation can be generalized asdescribed hereafter. The SKED engine objective function can include oneor more of the following cost items, (i) resource startup/shutdown costand no-load cost, (ii) resource incremental energy cost, (iii)transaction cost, (iv) ancillary services (i.e., regulation andreserves) procurement cost and the like.

Each SKED engine can include one or more of the following constraints inSKED engine formulation, (i) load balancing, (ii) ancillary servicesrequirements, (iii) resource capacity, (iv) resource ramp rate, (v)resource temporal constraints (minimum-run time, minimum-down time andthe like, (vi) maximum number of startup/shutdown, (vii) daily energyconstraints, (viii) emission, (ix) transmission security, and the like.

In one embodiment, a system tool for dispatchers in power grid controlsystems of the present invention supports multi-intervalsecurity-constrained commitment and economic dispatch as time-coupledco-optimization for SKEDi. SKED 2 can support interval based resourceavailability and operation mode. The resource qualification logic forSKED2 is: (1) With the assumption that the period between executionstarting point and target time of first interval covers the executiontime and case approval time: (2) Resources available for economics:startup time (choice to include notification time) and minimum run timeshould equal to or less than respective qualification time parametersdefined for commitment. Qualification time parameters are the thresholdparameters associated with time, such as startup time, as part ofqualification rules. Each interval of SKED2 can see the samequalification time length, so that eligible resources won't beover-restricted in far intervals. (3) Resources available for economicsthat have significant impact on transmission constraints: startup time,such as choice to include notification time, and minimum run time shouldbe equal to or less than the respective qualification time parametersdefined for committing resources that have effective impact on anon-system interface transmission constraint. The qualification timeparameters for resources brought on for constraint control can bedifferent from those of economics-brought-on resources. “Called on forconstraints” can continue to be determined in two steps 1) initiallybased on resources' shift factor greater than a user-defined thresholdsand 2) then re-qualified based on the binding status of the effectedconstraints and the user-defined min-run and startup time parameterthresholds and the resource's shift factor. The resource's shift factorrepresents the change in flow on a transmission corridor to anincremental injection at a resource's electrical bus.

SKED2's commitment capability can be limited by its qualification timeparameters only. SKED2 should focus on the resources that can bequalified in its domain. Any resources that do not meet thequalification rules can follow the commitment schedules in the latestCOPt, which may come from the day-ahead schedule or SKED1.

As a non-limiting example, a two-hour min-run time resource should beallowed to be committed for the last hour of a two hour SKED2 run. Theboundary issue cannot be avoided and can be resolved upon COPt updateupon User approval of the resource commitment and in the following SKEDengine runs.

Decommitment can apply to online resources per user defined type thatare not flagged as must-run and not include other qualification rules.Similarly, boundary commitment conditions need to be considered, e.g., aresource that will have met its min-run-time after the first hour of thesecond-hour SKED2 study period may be decommitted for the last hour.

“Emergency” resources are not qualified for SKED2 commitment.Qualification parameters can be defined for different SKEDs. Supportmixed integer programming based unit commitment functions for SKED2eligible resources include but are not limited to, resourcestartup/shutdown models and the like. Daily maximum startup andminimumUp/minimumDown constraints are respected.

The enforcement of reserve requirements at ancillary services area andreserve zone levels can be needed when the reserve zone constraints arebound from time to time in a selected ancillary services marketclearing. If an individual resource ancillary services assignment isavailable, then these individual assignments are respected in SKED2. Anancillary services area is an area in which ancillary services areprovided by resources within the area.

In one embodiment, each SKED engine can support flexible study intervalconfiguration, including but not limited to case start/end time and caseexecution frequency, with variable time steps. As a non-limitingexample, the variable time steps can be, a 15-minute interval for thefirst hour, a 30-minute interval for the second hour and the like. As anon-limiting example, target intervals can be anchored to the 5 minuteclock cycle, and the like. SKED 2 sequence execution can be configuredto be either automated case execution or manual case execution. EachSKED engine can support multiple study scenarios for high, medium, andlow load forecast values. Each SKED engine can support intervaldependent function configuration.

In one embodiment, enforcement of forward envelope bounding can beconfigurable for an SKED case. Resource qualification parameters can beconfigurable for each type of SKED for economic and transmission controlcommitment separately. The qualification parameters include, but are notlimited to, resource type and startup, option to include notificationtime, minimum run-time and the like.

In one embodiment, the coordination among SKED engines can be throughCOPt.

The next interval's dispatch megawatt range, following the last intervalof the current SKED I solution time horizon, in COPt may be used tocalculate envelope for SKED i+1 's dispatchable resources. A global flagcan be used to switch off the envelope function.

For SKED1 commitment decisions in COPt, which may be overridden byoperators, that can be acknowledged by operator, SKED2 can honor them.SKED2 is able to fine-tune the start ups for qualified resources bysliding them forward. Start up notification time and the outage statuswithin the current SKED2 time frame need to be considered during thedecision making. With the assumption that the start up has been decidedand made in COPt, SKED2 does not consider the related start up cost. Thedifference between the SKEDi calculated start up costs and actuallystart up costs due to cold/intermediate/hot start up status change canbe make-whole. SKED2 is able to shut down the qualified resources asneeded if their min run time limits have been met

In one embodiment, SKED 2 has data interfaces with the following systemcomponents: (1) day-ahead schedule to initialize COPt resourcecommitment status. (2) COPt. Via COP2, SKED 2 to provide COPt withresource schedule updates and dispatch megawatt. SKED2 will also receiveresource schedule updates from COPt for future intervals. (3) stateestimation and real-time contingency analysis. SKED2 to obtain real-timenetwork topology and real-time generation/load megawatt values fromstate estimator. Real-time transmission constraints can come from realtime contingency analysis. State estimation may also provide real-timebus load distribution factors for SKED engines. (4) outage schedule.scheduled transmission outages are available for SKED2 to determinenetwork topology of each SKED2 interval. (5) load forecast.

One example of a business process sequence of the process begins withinitialization. COPt can be initially populated with the day aheadmarketing and reliability assessment commitments. For SKED2, the units'initial status (on/off) and megawatt level can be obtained from a stateestimator. The first interval duration can be equal to the firstinterval target time (interval ending time) minus the study kick offtime. For SKED2. the state estimator topology can be used for allintervals.

The following information can be directly obtained or derived fromenergy management system: (1) resource's real-time status (on/off); (2)resource's output megawatt; (3) time resource has been running orshutdown; (4) accumulated number of startups during current day: (5)transmission equipments real-time status (in/out).

FIG. 9 illustrates the sequence of execution of SKED2, showing itsgranularity, launch time, period covered and frequency (SKED1 and 3 arealso shown for comparison purposes).

As a non-limiting example, in one embodiment, three steps are in eachSKED engine study, (i) case creation, (ii) case execution and (iii) caseapproval. For SKED2, each step can be either auto or manual. Theautomation of case creation and case execution can be coupled together,as a non-limiting example if the case execution is auto, the casecreation is also auto.

In auto execution mode, the study is performed based on a time schedule.When the case is created, the configuration parameters, manualoverrides, can be initialized from a case template and the execution isfollowed Immediately after the initiation is completed. The user canmodify the entries in the template; the modification can becomeeffective on the next scheduled execution and will continue effectiveuntil next modification or termination time expires. In auto approvalmode, once the case execution is completed and solution is uploaded tothe market database, the approval process will proceed subject topre-defined condition checks and the comprehensive operating plan isupdated upon case approval.

In manual execution mode, the user manually creates the case. The casecan be initiated from a previous case, or from the template. User willhave opportunity to perform additional manual override before executingthe case. With manual approval mode, user will have opportunity toexamine the solution but user cannot alter the solution. If necessary,user can make additional overrides and re-execute the case. The userneeds to manually initiate the approval process.

In one embodiment, for SKED2, envelope limits can be calculated for thelast SKED2 interval based on the dispatch megawatt of the nearest futureinterval from SKED1 and dispatch ramp rate.

An energy system tool for dispatchers in power grid control systems canuse a ramp-rate model. For each interval the average ramp rates can becalculated based on the current state estimator generation level and themaximum/minimum level achievable in the corresponding interval duration.

SKED2 can support the following resource control modes: commitment:fixed; committable (for economics or for transmission); decommittable;slidable, energy: fixed; dispatchable; block loading; regulation: fixed;dispatchable (may include part self scheduled); spinning: fixed;dispatchable (may include part self scheduled) and the like. For SKED2,the definition of the control mode for a given resource can consider theresource characteristics, including but not limited to, type, start uptime, notification time, minimum up time, resource's shift factors,decisions taken by other processes, e.g. a combustion turbine has beencalled on by the operator, SKED1 has recommended to commit a combustionturbine, ancillary services clearing has assigned regulation/spinningreserves to a given combustion turbine, and the like. Ancillary servicesassignments can be honored by SKED2. That is, as a non-limiting example,for offline combustion turbines provide spinning, it can be committed bySKED2 as long as it can provide the assigned amount of spinning reserve.Operator commitment overrides can be treated as fixed.

In one embodiment of the transactions model the following can beprovided, fixed transactions by schedules, dispatchable transactions,emergency interchange schedule and the like.

In one embodiment of the transmission model, identified unscheduledoutages are made by comparing real-time state estimator againstscheduled outages and are presented to the operator on the marketoperator interface for review and inclusion to the next SKED engineexecution. Model transmission security constraints can include alleviateoverload constraints, watchlist constraints, simultaneous feasibilitytest constraints and the like. Constraint the right hand side andpenalty cost for alleviate overload and flow-gate constraints can beinterval dependent in SKED.

Load forecasts can be provided for each of the SKED2 intervals on thecontrol area basis. Bus loads with each control area can be determinedbased on the control area load forecast and the bus load distributionfactors. User can have the ability to vary the control area based biaswhich is not related to intervals.

The bus load distribution factors may be determined based on the lateststate estimator bus loads or based on a static load model.

In one embodiment the objective of SKED2 is to minimize theprice-weighted value of energy bids/offers cleared over the studyperiod, while satisfying the specified security constraints. Operatingcost components—start up/shut down, no load cost and incremental energycost; penalty terms—to penalize the constraint violations; Constraints;Generation/Demand Side Response; Capacity constraints—economic,regulation, control, and spinning (min and max); Temporalconstraints—start-up/shut-down/economic dispatch ramp rates (use nominaleconomic dispatch ramp rate for start upshut down ramping), min run/downtimes, max # of starts; Block loading; Transmission; Constraints—Theresource's shift factor based linear combination of net megawattinjections; load balance constraints—injections and withdrawals;ancillary services requirements constraints—for intervals with nosolution spinning/regulation reserve constraints at ancillary servicesarea (and reserve zone) level(s) can be enforced; Controls(variables)—commitment decisions for qualified combustion turbines,megawatt cleared, constraint violations; Time coupling—SKED2 is a timecoupled economic dispatch over the study horizon. The introduction ofthe time dimension into the dispatch formulation, as compared to asingle interval sequential solution, provides economic (pre-ramping) aswell as strategic value (profile of generation instructions).

In one embodiment, SKED2 provides solutions for three demand scenarios:medium, high and low. The solutions can be, except for the common stateestimator initial conditions, totally independent.

Another embodiment of the present invention is an adaptive modelmanagement as shown in FIG. 5 and includes adaptive constraintmanagement which a general term to represent a process or managementsubsystem for transmission constraint in an adaptive fashion.

Adaptive constraint management intelligently preprocesses transmissionconstraints based on historical and current system conditions, loadforecasts, and other key parameters. It predicts potential transmissionconstraints and allows smoother transmission constraint binding.

The resource “profiles” may contain parameters such as ramp rates,operating bands, predicted response per megawatt of requested change,operating limits and the like.

In some embodiments, the demand system of the present invention includesa demand forecast integrator that provides centrally managed demandforecast information. In one embodiment, demand forecast integrator hasone or more of the following high-level requirements: flexibility inaccepting load forecasts from different engines; standard, butextendable software for merging load forecasts from different loadforecast engines: supporting operator load forecast overrides; usingindustry standards, such as XML for Input and output data; using Webservices as the primary interface means; physical persistence layer thatsupports auditing, archiving and historical data (for perfect dispatchapplications); a graphical user interface, and the like.

In some embodiments, the demand forecast integrator supports the input,management, and output of load forecast data. The demand forecastintegrator does not itself perform load forecasting itself, but doesprovide one or more of the following: a programmatic interface that willaccept area load forecasts from external load forecast engines; a userinterface that will allow an operator to review input load forecasts andto modify load forecasts via an override capability an ability to mergemultiple load forecasts that cover the same time range to form acomposite load forecast; a programmatic Interface that will allowconsuming applications to query for load forecasts covering a range ofareas, intervals, and time ranges; a persistence store to hold loadforecast data, and the like

In some embodiments, load forecast represents a load forecast engine aswell as a repository of load forecasts for Habitat applications. Loadforecast and demand forecast integrator can cooperate in that loadforecast can be a provider of load forecast to demand forecastintegrator, while demand forecast integrator can merge load forecastsfrom load forecast and other 3rd party engines.

FIG. 10 illustrates one embodiment of a component architecture and dataflow for the demand forecast integrator in a sample implementation.

In some embodiments, the load forecast sources in FIG. 10 provide loadforecasts that can be produced at varying frequencies. As a non-limitingexample, sample frequencies can be: ultra short which can be everyminute; short (every 5 minutes), and medium term (every hour). The loadforecast results managed by these external engines can be periodicallysent to the demand forecast integrator by a variety of means, includingbut not limited to web service calls. Either the existing engines can bemodified to make these calls or an adapter can be created to facilitatethe web service calls.

The primary building blocks for the demand forecast integrator are theload forecast area (load forecast A), load forecast associated with aload forecast A, and load forecast source (load forecast S) whichsubmits load forecast for load forecast A. In some embodiments, loadforecast can be modeled in a tree structure so that load forecast for ahigh-level load forecast A may be derived from subordinate load forecastAs′ load forecast. FIG. 11 shows a collection of “leaf node” loadforecast As (in red circles) and a two aggregate level load forecast.

In some embodiments, load forecast can be modeled as a series of (time,megawatt) values and linear interpolation can be used to determine theload forecast for a given time. There is no limitation to the distancebetween two consecutive load forecast times for a given load forecast Aand load forecast S. For a given load forecast A, multiple load forecastS can submit load forecast S that may cover common time Intervals.Demand forecast integrator may have an ability to merge multipleoverlapping load forecast S to form a composite load forecast thatreflects modeled weighting factors associated with each submitted loadforecast. FIG. 12 shows an example of load forecast construction for asingle load forecast area.

In some embodiments, the following objects can be modeled in the demandforecast integrator data store and used or generated by the demandforecast integrator application: load forecast area; load forecast areahierarchy, which is effectively dated, that is, the parent-childrelationship in the load forecast hierarchy may vary with time; loadforecast A hierarchy scaling factor is included in each load forecast A,which can be used to calculate load forecast at the aggregate levels;“leaf” load forecast areas, which belong to one and only one aggregateload forecast A; “Aggregate” load forecast areas; load forecast sources,which include weighting factors for submitted load forecasts. Here,weighting factors are defined as profiles relative to the current time;load forecast by source; load forecast override; for composite loadforecast only; composite load forecast; historical composite loadforecast and override, and the like.

In some embodiments, load forecast may have a varying time distancebetween two consecutive data points. The values of a load forecast curverepresent a specific data type. For example, the values may be megawatt,peak megawatt, or megawatt h. A given curve contains values of just onetype, the type being specified as part of a submission. The list ofsupported load forecast type is configurable and implementation fordifferent load forecast types may vary. For example, when a loadforecast engine was outaged for an extended period of time, no loadforecast is available from that load forecast engine for a period oftime.

In some embodiments, the demand forecast Integrator may provide acallable interface with one or more of the following characteristics:SOAP (simple object access protocol) over HTTP web services; a publishedWSDL; operations that allow a load forecast source to submit loadforecasts for load forecast areas, and the like. Each submittal can havethe following properties: name of the load forecast source; name of theload forecast area (only leaf-level load forecast A areas are permittedfor submissions) or group of load forecast A's; a set oftimepoint/megawatt pairs to specify the load forecast and by loadforecast A if multiple load forecast A's can be submitted in oneoperation; classification of submittal type (megawatt, megawatt/hour,peak megawatt, and the like) can be needed; a timestamp of the loadforecast that can be used to prevent overwriting more current data withlate-arriving stale data; operations that allow an operator of themarket operator interface to submit load forecast overrides; operationsthat allow a load forecast consumer to query for the following (all needto support optional filtering by time of submission and/or effectivetime): load forecasts by load forecast source; load forecasts by loadforecast area (leaf or aggregate levels); load forecasts inuser-provided or source-data driven time steps, and the like. Time stepsmay be provided in one of a number of ways: a single number or vector oftime steps; overridden load forecasts can be flagged in the queryresponse; composite load forecasts with override; historical compositeload forecasts with override as of a user-provided time; and the like.As a non-limiting example, administering the load forecast A, loadforecast hierarchy, load forecast S weighting factors, and the like, canbe done via the market operator interface.

In some embodiments, the demand forecast integrator uses a pluggableapproach to authentication and authorization. The authentication andauthorization requirements can be often dictated by the deploymentenvironment, and demand forecast integrator needs to be capable ofoperating in a secure environment. This can be accomplished by designinginterface classes for the authentication and authorization subsystemsand providing appropriate implementations dictated by the deploymentenvironment, demand forecast integrator can provide an “out of the box”implementation for authentication and authorization, but the design mustallow for other implementations to be plugged in as required.

As an example, a given deployment may call for authenticating auser/password combination against active directory and enforcingauthorization checks at each web service operation based on a roleassigned to the authenticated user. The modeling and administrationneeded to support this scenario can be likely to be done by a corporateIT group. To be deployed in this environment, demand forecast integratorwould have to load a custom implementation for the authentication andauthorization interface that accesses the active directory and theauthorization data store that can be used.

Such an “out of the box” implementation provided by the demand forecastintegrator is suitable for running within a trusted environment thatrestricts access to only those client machines that can run on the sameLAN and behind a firewall. Such an implementation can automaticallyauthenticate any client that attempts to communicate with demandforecast integrator via the web service interface. Every authenticateduser can be permitted to execute all operations supported by the webservice interface.

In some embodiments, the demand forecast integrator applicationmaintains a transactional data store to hold submitted and calculateddata and one or more of the following rules may apply: submitted data(i.e. load forecasts and overrides) is stored at the time it can besubmitted; each submission has an associated transaction ID; each querythat results in a composite load forecast being created is stored at thetime it occurs and it has an associated transaction ID. The demandforecast integrator application accesses necessary model data includingbut not limited to load forecast areas, sources, area hierarchy, and thelike, from the data store but it need not be maintained by the demandforecast integrator application. Population of model data is achievedthrough an external interface; the demand forecast integratorapplication accesses necessary historical data in response to queries onhistorical composite load forecasts and overrides. Population of thehistorical data store is not performed by the demand forecast integratorapplication. Persistence technology, such as a relational databasemanagement system, supports standard approach to auditing, rollback,flexible extension and the like.

In some embodiments, the method of calculating the composite loadforecast may be re-implemented with different algorithm to suitdifferent customer needs. Over time, the demand forecast integratorcontains a series of composite load forecast construction methods thatcan be configured to match project needs. These construction methods canbe configured during implementation and need not be dynamic userchoices. The set of load forecast values that can be processed by demandforecast integrator may be extended in the future. In one embodiment thedemand forecast integrator is relational database management systemneutral.

In some embodiments, the demand forecast integrator produces compositeload forecasts for load forecast areas taking into account overrides andsubmitted load forecasts. One or more of the following rules may apply:the composite load forecast covers the time interval and load forecast;a specified in a query can be submitted by a demand forecast integratorconsumer (which also includes the market operator interface), and thelike. When multiple load forecasts for a load forecast A cover the sametime interval, the resultant composite load forecast can apply modeledweighting factors for each load forecast based on the load forecastsource. As a non-limiting example, the composite load forecast for timeinterval t0-t1 can be a weighted average of the values provided byultra-short-term load forecast, short-term load forecast, mid-term loadforecast, and the like.

The weighting factor for each load forecast can be, (i) an attributeassociated with each load forecast source; (ii) overrides can be appliedto the composite load forecast produced in the above step; (iii) acomposite for aggregate load forecast can be supported; (iv) a compositeload forecast for a user-given time step size can be supported, and thelike. Interpolation may be required to provide composite load forecastin the user-given time step size; smoothing at transitional boundariesfrom one load forecast (such as a 5-minute load forecast) to another(such as an hourly load forecast) and be achieved through manualfine-tuning of the load forecast source weighting factor profile.

In some embodiments, load forecast source weighting factors can be usedto calculate the composite load forecast. These factors define theweight of a load forecast source over a series of relative times. FIG.13 illustrates how three load forecast S weights vary over a 90-minutesperiod. In this example, load forecast 1 can be the most accurateforecast for the short term forecast, load forecast 3 provide the mostaccurate forecast for the long term and load forecast 2 is in-between.

Aggregate load forecast areas can be those having one or more child loadforecast as in the load forecast A hierarchy tree defined in the demandforecast integrator. In one embodiment, a load forecast cannot besubmitted for aggregate load forecast As. However, a load forecast foran aggregate load forecast As can be calculated as follows: (i) eachload forecast A can be associated with a scaling factor, if it can benot provided, it is assumed to be 1; (ii) a load forecast of anaggregate load forecast A can be the sum of the scaled load forecast ofits immediate child load forecast As; distribution factors need not besupported by the demand forecast integrator, i.e., the demand forecastintegrator need not have the ability to calculate the load forecast of achild load forecast A from the load forecast of an aggregate loadforecast.

In some embodiments, the following business rules can be applied to loadforecast overrides: (i) an override can be inclusive in starting pointand exclusive in ending point; (ii) a new override will take precedenceover old override(s) if they have overlap in time axis; (iii) no smoothcan be applied to overrides, and the like. A use case, which is adescription of a system's behavior as it responds to a request thatoriginates from outside of that system, is illustrated in FIG. 14.

When the load forecast area hierarchy changes, one or more of thefollowing may occur: (i) the set of load forecast A's which belong to anaggregate load forecast A changes; (ii) a leaf load forecast A maybecome an aggregate and vice versa; (iii) a new load forecast A can beadded to the hierarchy; (iv) an old load forecast A can be removed fromthe hierarchy; (v) a load forecast A hierarchy transition can be handledin the demand forecast integrator as follows: load forecast submissionvalidation does not depend on the load forecast A hierarchy, as long asthe specified load forecast A is a valid entry; (vi) a composite loadforecast can be built according to the load forecast A hierarchy, andthe like. If the target time range of the composite load forecast spansover two load forecast A hierarchies, the resulting composite loadforecast reflect the two hierarchies accordingly.

Example 1 Use Case No 1 Submitting Load Forecast

This example illustrates adding new load forecast to one or more loadforecast areas for the respective load forecast source. The trigger isload forecast engine 1 which submits a new load forecast. The demandforecast integrator validates the submitted information: valid loadforecast source, valid leaf-level load forecast area, properly formedload forecast data points. demand forecast integrator overlays new loadforecast on top of existing load forecast for load forecast engine 1.Assuming that the submitted load forecast's minimum and maximum time areT_(min) and T_(max), all previous input from load forecast engine 1within T_(min) and T_(max) inclusively can be deleted. Demand forecastintegrator responds with a success message. As a post condition, loadforecast from load forecast engine 1 is updated in demand forecastintegrator. This example illustrates how new submittals contain betterload forecast than the previous ones.

Example 2 Use Case No 2 Submitting Load Forecast Overrides

This example illustrates adding load forecast override for one or moreload forecast areas. In this example, the trigger occurs when anoperator entered and saved load forecast override for a load forecast A.The market operator interface submits the overrides to the demandforecast integrator. demand forecast integrator validates the submittedinformation: valid load forecast source, valid leaf-level load forecastarea, properly formed load forecast override data and the like. Thedemand forecast integrator adds the override values for the loadforecast A. Assuming that the submitted override runs from T1 to T2, allprevious override within T1 and T2 is removed. The submittal may Includemultiple set of overrides with non-adjacent time periods for the sameload forecast A. Demand forecast integrator responds with a successmessage. The result is that demand forecast integrator containsadditional overrides for the load forecast A.

Example 3 Use Case No 3 Requesting Composite Load Forecast for theMarket Operator Interface

This example illustrates obtaining a composite load forecast for themarket operator interface. The trigger event is when the operatordisplays the composite load forecast for a specified time range. Themarket operator interface submits a request for the composite loadforecast for the requested time range and load forecast A. The demandforecast integrator validates the submitted information which includesvalidating: (i) the load forecast source; (ii) the leaf-level loadforecast area; (iv) from and to times; (v) time step, and the like. Thedemand forecast integrator constructs the composite load forecast or thedemand forecast integrator retrieves the composite load forecastdepending on the implementation. The demand forecast integrator respondswith the composite load forecast, including overrides. The marketoperator interface displays the composite load forecast and overrides ina graphical format. The returned information can contain the compositeload forecast before and after application of override.

FIG. 15 illustrates one embodiment of the demand forecast integratorsoftware architecture. The demand forecast integrator has externalclients that can be load forecast engines or consumers. Multiple loadforecast engines may submit load forecast periodically to the demandforecast integrator as frequent as every few minutes. Consumers mayquery composite load forecasts and override load forecasts randomly.External clients can be responsible for creating and submitting soapmessages based on the demand forecast integrator XML message schema andWSDL. The XML schema and WSDL can be published to external clients.

An external service layer can be provided. In some embodiments,multi-threaded CXF web service listeners can be serving the requests.CXF is responsible for XML validation and converting soap messages toJAXB.

An implementation of WSDL operations serves the requests and dispatchesthe requests to internal service layers for data transformation.

In some embodiments, transaction id can be generated in an internalservice layer. This transaction id is stored in and accessed from athread context within the thread that processes the request. Datatransformation to business objects can be required if the JAXB does notprovide good representation for functional operations such as mergingload forecasts.

A business layer can be provided that implements all functionalrequirements such as merging and overriding. Business objects can bedefined to support functional requirements. A set of functionaloperation interfaces is defined to operate these business objects.

A persistence layer can be provided. In some embodiments, businessobjects can be mapped to a relational database management system tableusing hibernate XML mapping files. One example of structure of aninternal service layer is illustrated in FIG. 16.

In one embodiment, all internal service implementation classes implementa base class that takes care of transaction management. The internalservice implementation class can implement an execute method that canwrap around the whole request within the thread process.

FIG. 17 depicts an example of the demand forecast integrator physicaldata layer. Operator manual overrides can be allowed to change thedemand forecast integrator results. Those overrides are submitted to thedemand forecast Integrator, and have impacts on all new cases and caserepopulation. An override should stay until it's manually deleted. Inthis way, overrides consistency could be preserved within the entiresystem.

Another core function of the system tool for dispatchers in power gridcontrol systems of the present invention is after-the fact analysis.After the fact analysis aims at providing a framework to conductforensic analysis. After the fact analysis is a decision-support tool toevaluate operational and financial performance, assess impacts due tohistorical or potentially new system events and/or conditions, and seekchanges as necessary.

After the fact analysis is a product for systematic analysis of pastevents and practices, and Is illustrated in FIG. 18. It can be used toestablish quantitative assessments of how do specific events andpractices affect system performance. A key characteristic of after thefact analysis can be the use of actual system operational data, whichhelps establish a suitable level of credibility and practicality of theafter the fact analysis analytical results. After the fact analysis canbe designed to support many different types of analysis within aconsistent framework. Different types of analysis can be implemented asdifferent types of use-cases. In fact, after the fact analysis can be aclass of use-cases that focus on “Day-After” analysis of the performanceof actual system dispatch results, including the impacts of specificevents. It will be appreciated that after the fact analysis can be for atime period other than a day after. After the fact analysis could bebuilt on any existing market systems and be considered as an add-onoff-line “Day-After” analysis tool. The discussion below focuses on thedesign for perfect dispatch, illustrated in FIG. 19, as it exists withinthe overall after the fact analysis framework.

After the fact analysis is a comprehensive business solution used in anoff-line, after-the-fact environment to study the real-time dispatchperformance of any previous day within the data retention period.Requirements for after the fact analysis are defined by the use-cases interms of their engineering business functional capabilities, andspecific levels of integrated data interfaces and user interfaces.Requirements that can be common to multiple use-cases are presentedbefore the individual use-cases requirements.

One of the business objectives of after the fact analysis is to assessthe performance of system dispatch by comparing performance metrics foractual dispatch results with metrics for reference case dispatchresults. Performance metrics can be a set of quantitative metrics thatreflect system efficiency and security concerns. By way of illustration,and without limitation, using total production cost as a metric forefficiency, after the fact analysis can calculate the cost associatedwith actual dispatch and compares it against the cost associated withthe reference dispatch.

Another business objective of after the fact analysis is to analyzeperformance impacts associated with specific operating events, e.g.excessive deviations of tie-line flows from the scheduled levels. Bysimulating specific events and calculating their quantitative impacts onsystem performance, the after the fact analysis should generateinformation that could help identify reasons that contribute to theperformance gap between actual system operation and an idealizedreference condition. It is important that the impacts for each event beclearly identified, and that multiple events may be analyzed within agiven after the fact analysis study. To ensure clarity in interpretingthe impacts of each event, the after the fact analysis can simulate eachevent individually, and calculate its impacts with respect toappropriate Performance metrics. The performance metrics can then becompared with the Reference Case and actual system operation history.

The expected results from each after the fact analysis study include oneor more of; re-constructed actual system operation history from recordedreal-time data; re-construction of recorded real-time data does not relyon results from any optimization application security constrained unitcommitment (SCUC), which is a process to perform unit commitmentsubjected to transmission constraints in a power system. In oneembodiment, after the fact analysis includes recorded real-time datawith the following data for each time period over the after the factanalysis study interval, actual: load; unit output; unit commitmentstatus for each scheduling Interval; network topology; constraint flowsand the like.

In some embodiments, after the fact analysis uses the same optimizationdispatch engine as the scheduling functions. It solves thecommonly-known security constrained economic dispatch (SCED) problem,which can be the general optimization framework applicable to variousscheduling tasks.

The definition and creation of reference case dispatch results is basedon specific business rules, such as use actual demand instead offorecast demand, and in general requires solving optimizationapplications (e.g. SCUC/SCED). Different use-case types can havedifferent rules for construction reference case dispatch results. One ormore scenario results from the set of specific events associate eachafter the fact analysis study. Each scenario contains specific changesto input data for the reference case dispatch results case (e.g.increase reserve requirement by 100 megawatt over the reference casedispatch results reserve requirement). In general, each scenariorequires solving optimization applications (e.g. SCUC/SCED).

Different use-case types can have different types of scenario results,which may include one or more of: performance metrics calculated forrecorded real-time data, reference case dispatch results, and eachscenario; appropriate summary and drill-down details for eachperformance metrics; comparison of different cases including but notlimited to, recorded real-time data, reference case dispatch results,scenario and the like. History of system/resource operations can becaptured via standard data-recording functions and can be made availableto after the fact analysis. After the fact analysis does not performgeneral purpose real-time data recording.

After the fact analysis provides a tool that collects appropriatehistorical data from the archived market cases that include stateestimator and alleviate overload files to construct recorded real-timedata.

After the fact analysis studies are conducted with extensive use ofpre-defined default configurations and data settings, and automated datagathering. To further simplify after the fact analysis usage, differenttypes of after the fact analysis use-cases are constructed to supportdifferent default configurations and settings. Use-cases can beimplemented as study modes in market database. A user selects specificuse-case when running an after the fact analysis study. Each after thefact analysis study can be identified by a user-defined name andincludes set of input & output data consistent with the use-case (i.e.study mode) type it belongs to. Input data to after the fact analysisincludes one or more of: recorded real-time data is abstracted fromrecorded operation, as described earlier, reference case dispatchresults data includes data from consolidated operating plan, which canbe a set of tables in market database third-party data sources, such asactual unit ramping performance, and miscellaneous user-entries;scenario data includes Impact factors specified by the user and may inthe future also include data from third-party data sources.

After the fact analysis can utilize a transmission security model. Afterthe fact analysis results can be subject to transmission securityconstraints. The set of transmission constraints can be as defined inthe market database/the comprehensive operating plan. In addition, afterthe fact analysis may be configured, if necessary, to interface withsimultaneous feasibility test to identify constraint violations beyondthose initially contained in the market database/the comprehensiveoperating plan.

Output data from after the fact analysis includes one or more of:re-constructed recorded real-time data and performance metrics;reference case dispatch results and performance metrics; scenarioresults and performance metrics for each and every scenario defined forthe study. There are various factors that have impacts on after the factanalysis system operations. For each scenario in an after the factanalysis study, the user may define a specific set of impact factorsthat collectively represent a specific event or a set of events.

In one embodiment, the after the fact analysis system needs to performat least the following major core functions: Interacting with capturedhistorical data; enabling users to define studies including referenceand scenarios; conducting batch studies and comparisons of history,reference, and multiple scenarios; analytical modules: optimizationengines, security analysis; the market operator interface to set upstudy cases and to evaluate study results and the like.

To compare what has been taken place in the study day, after the factanalysis collaborates with various systems that capture and record powersystem operations data in real-time. This data may include but is notlimited to system performance data such as load, frequency, tie-lineerror, reserve margin; decision data such as dispatch instructions, unitcall on/call off; and system events such as topology changes, constraintviolations, other disturbances.

The principal category of captured power system data of after the factanalysis is the data preserved in state estimator and alleviate overloadfiles such as: unit commitment status; unit outputs (active andreactive); active and reactive bus loads; active and reactive branchflows, and; active and reactive constraint flows.

State estimator data can be preserved in archived market solutions(SKED2/general control application) and can be extracted from thearchived cases, consolidated and made available to after the factanalysis by the after the fact analysis pre-processor. An after the factanalysis pre-processor can be implemented as a scheduled job in a marketdatabase that runs in early morning hours to extract the actualoperational data and make it available for subsequent after the factanalysis studies.

In addition to state estimator and alleviate overload data, severaltypes of data can be stored in the market database and sourced fromarchived files as well. While this data can be exported to after thefact analysis from market database via the standard exporter mechanism,it can be pre-processed by the after the fact analysis pre-processor inorder to improve the performance of after the fact analysis studies.This data includes but is not limited to: load forecast; regulationreserve requirements; operating reserve requirements; unit megawattschedules; unit megawatt limits; demand resource schedules; securityconstraints; scheduled outages; transmission security constraints;scheduled Net Interchange and the like.

In some embodiments, each after the fact analysis study can beassociated with a specific after the fact analysis use-case (i.e. afterthe fact analysis study can be implemented as case or set of cases inthe market database whose study Mode corresponds to the after the factanalysis use-case). Many study datasets may be involved in a daily afterthe fact analysis-Low study. Each dataset can be associated with the dayand can be uniquely identified by study name (e.g. after the factanalysis-Low_yyyy-mm-dd_studyname. Each study contains multiplescenarios: recorded real-time data, for actual History, reference casedispatch results, for reference, scenario, for Impact scenarios, and thelike. Each scenario can contain one or more impact-factor models.Performance metrics are evaluated and compared for each scenario.

The data structures required for an after the fact analysis studyinclude but are not limited to: (1) Study (use-case) type, which definesthe type of study being conducted, with each use-case having a set ofanalyst-configurable parameters with default data values. (2) Studycontrol, which defines the run time parameters such as duration of thestudy. (3) Reference case, which defines the input data set required forconstructing the reference case, such as whether actual or nominalreserve requirements can be used, as well as configurable parameter forexecuting the reference case such as an option for allowingre-commitment. (4) Scenario case, where each study can have one or morescenario cases. Each scenario can be an impact analysis of the combinedeffect of a collection of impact data items against the reference case.Other than having the common reference case, each scenario can beindependent of any other scenarios. (5) Impact factor data items, whichare operating parameters that may impact the system performance, such astie-line schedule, unit ramp rate, reserve requirements. For each dataitem, user can enter either the absolute value or the deviation from thereference case. Multiple impact-items may be grouped intonamed-templates for convenient re-use in other studies. (6) Performancemetrics, which are default data items that compares the result of eachscenario case verse the reference case, such as production cost, reservecapacity, and the like.

In one embodiment, three functional modules are related to the executionof an off-line study case. The business logic for these functionalmodules primarily resides in the market database. As a non-limitingexample, separate queues can be deployed to avoid adversely affect theon-line scheduling functions, and to ensure appropriate performance forafter the fact analysis.

The first module is a construct Input data module which prepares all theinput data for the study. A user analysis defines the study. This modulethen: (1) collects all the data that represents the actual dispatchperformance; (2) prepares the input data for constructing the referencecase; (3) gathers the impact data items to construct the scenario cases,and the like. For efficient purpose, the impact data items for ascenario can be exported as a “delta file” such as deviations from thereference case data, and the like.

In addition to the existing market database-based mechanism forexporting data to analytical engines for studies, that is exporterapplication and views, a dedicated after the fact analysis pre-processorapplication can be used for collecting operational and other input datafor after the fact analysis from archived case files. This datapre-processing can be scheduled to run before daily after the factanalysis-Low study. It can be implemented as a new after the factanalysis_PREP study mode in the market database.

The second module is a batch execution module, where the reference caseand the scenario cases can be grouped as a batch. An after the factanalysis-Low study may also include only the reference case. From theanalytical module standpoint, they are separate dispatch cases. In thedatabase, they can be linked together for processing and comparison.Multiple scenarios and reference case can be executed in parallel.

The third module is a compile study results module which consists ofdatabase procedures that can be linked to new after the fact analysisdisplays in the market operator interface. Based on the batchinformation (i.e. case data) and the performance metrics defined for thestudy, this module processes the case solutions and performs theautomatic case comparison to provide impact analysis results.

A scenario designed to simulate the effects of short term demandforecast errors, for example, may include changes to impact factorsrepresenting regulation requirements and operating reserve requirements.Each impact factor can be typically modeled by input data parameters.The effects of impact factors on system performance can be determined bysolving each scenario with appropriate input data parameters thatcorrespond to the impact factors.

The set of impact factors can be very comprehensive. Examples of Impactfactors include but are not limited to: (1) system and area loadchanges; (2) zone and area regulation requirement changes; (3) zone andarea reserve requirement changes; (4) unit limit and ramp rate changes;(5) unit commitment status change; (6) unit minimum UP and Down timechanges; (6) security constraint change; schedule transaction megawattand price changes; (7) constraint limit and contingency changes; (8)topology changes, and the like.

An optimization model can be utilized. In some embodiments, the resultsof reference case dispatch results and scenario's can be solutions froma SKED engine. The optimization model for each solution can be based onthe formulation framework of a general control application problem andcan be characterized by a commitment optimization that allows theresources to be recommitted, instead of forcing each resource to followthe actual commitment schedules over the after the fact analysis studyinterval.

Even when commitment is permitted, re-commitment can be performed onlyfor units designed as eligible & available for re-commitment: dispatchoptimization. This allows the resources to be permitted to be optimallyre-dispatched for the after the fact analysis study interval, and caninclude: (1) The basic re-dispatch model can be based on the generalcontrol application SKED engine formulation, where all time-intervalscan be optimized simultaneously as one multi-interval commitmentproblem; (2) in addition to the basic model, an alliterative extensionis provided that will solve the multi-interval re-dispatch problem as aseries of single-interval dispatch. This alternate approach can use thesolution dispatch megawatt at the end of each interval “t” as theinterval as the initial condition for each interval “t+1”, and allowsre-dispatch for the “t+1” interval to be bound by the resource ramp ratethe interval “t+1”; (3) Loss optimization allows for the lossminimization among those equipments designated in network model; (4) Useof a resource model, where in some embodiments, resources can be modeledbased on the same information used in day-ahead and intra-day schedules,including but not limited to capacity & ramp rate limits, heat rate &fuel model, and the like. The basic model parameters can be contained inthe market database, including the actual operator's input, overrides,and impact-factor changes for specific scenarios.

A system requirements model can be used for system parameters, such asdemand forecast, reserve requirements, and the like, that is based ondata in the comprehensive operating plan, and includes overrides andimpact-factor changes for specific scenarios.

In some embodiments, changes to one or more of the following data valuefrom the level used as input is applied to determine reference casedispatch results. Each scenario may contain multiple changes includingbut not limited to changes for (1) Regulation requirements forreflecting effects of tie-line errors, deviations from nominal unitramping performance, very-short-term load forecast errors, uncertaintiesin wind-generation; (2) Reserve requirements for reflecting effects ofday-ahead and intra-day forecast error, uncertainties inwind-generation; (3) System and area load to reflect effects ofday-ahead and intra-day forecast errors and uncertainties indistributed, non-observable generation; (4) Tie-line schedules toreflect changes to scheduled tie-line equanimities; (5) Scheduledtransactions for changes to scheduled tie-line equanimities; (6) Unitmegawatt limits, including units that have fixed megawatt schedules, andfor reflecting effects of units not-following dispatch instructions, forproviding voltage support, including but not limited to, minimummegawatt generation and must-run; (7) Unit Commitment status where usersare able to change commitment statuses of selected units to reflecteffects of day-ahead and intra-day forecast errors, providing voltagesupport; (8) Unit operational parameters such as up/down ramp rates, minup/down and initially on times, allowing a user to change theseparameters to reflect actual conditions, such as after unplanned events,and to create ‘what-if’ scenarios; (9) Load resource operationalparameters for uncertainties intrinsic in modeling of this type ofresources; (10) Security constraint limits for changing operatingconditions, errors in bus-load distribution factors, and the like.

With the present invention it is possible to calculate severalperformance metrics based on actual and/or calculated megawatt dispatchresults. The set of performance metric's can include: economicoperation; bid production cost; daily percent of perfect; bid productioncost savings; startup cost for incrementally committed units; startupcost savings; percent savings; year to date (YTD) running average;megawatt purchase cost (based on contract price paid to generators forinjecting power to the grid); fuel consumption; emission; losses;physical operation; total generation; regulation reserve capacity;operating reserve capacity; unit commitment schedule; unit megawattoutput; grid security; megawatt loading levels for pre-definedconstraints in the Watch List; constraint violations, and the like.

As a non-limiting example, the set of performance metrics describedabove can be organized as follows: area grouping: by control area,generating company, plant, unit fuel type (coal, gas, hydro, wind,nuclear, other), division (sub-area); time grouping: daily, studyinterval (future function, when the study Interval covers multipledays), and the like.

Example 4 Perfect Dispatch Use-Case 1 After the Fact Analysis-Low: IdealReference

The business objective in this example is to re-dispatch and/orre-commit results. This is used to a create reference case dispatchresults condition which can be designed to show what can be the best(e.g. lowest cost) dispatch result that would be theoretically possible,even though these perfect system conditions, which can be input to theafter the fact analysis, may not exists in the real physical system.This use-case can be named after the fact analysis-Low, where Low refersto the reference case dispatch results being the lowest cost idealcondition. From the low-cost reference, after the fact analysis-Low,each scenario, which simulates effects of impact factors, can beenvisioned to produce cost-adders that would be added on top of thereference case dispatch results. The adders gradually fill from thebottom up and reduce the gap between recorded real-time data and afterthe fact analysis-Low reference case dispatch results.

The set of input data used for determining reference case dispatchresults for after the fact analysis-Low is as follows: actual load;nominal/typical regulation requirement; nominal/typical reserverequirement; actual network topology; nominal (i.e. perfect) unitoperating conditions: capacity and ramp limits, and the like. The actualcommitment schedules from recorded real-time data can be used as thestarting point for unit commitment. Incremental re-commitment ofqualified units is allowed in after the fact analysis-Low when theactual unit plan does not meet load, regulation and reserverequirements. As a non-limiting example, the regulation and reserverequirements can be: a combustion turbine and/or steam based onqualification time; nuclear and hydro can be mostly self-scheduled, andthe like.

Example 5 Perfect Dispatch Use-Case 2 After the Fact Analysis-High:Practical Reference

In this example, the business objective is to re-dispatch and/orre-commit results to create reference case dispatch results conditionthat shows incremental and/or decremental changes in performance metricsover the actual dispatch (recorded real-time data). The differencesbetween recorded real-time data and reference case dispatch results, forthis use-case, can be results of certain “not-so-perfect” factors thatadversely affected the “actual dispatch”. In contrast with the previoususe case (after the fact analysis-Low), which uses a “lowest cost”reference dispatch, this use case uses a reference dispatch that is morepractical than the ideal condition. This reference dispatch isrelatively close to the actual recorded real-time data dispatch, andthus likely to have higher cost than the reference dispatch of after thefact analysis-Low use case. Hence the name after the fact analysis-Highfor its higher-than-lowest cost reference dispatch.

The set of input data used for determining reference case dispatchresults for after the fact analysis-High can be as follows: actual load;regulation requirement that reflects actual conditions of the day (datavalues externally supplied to after the fact analysis); reserverequirement that reflects actual conditions of the day, which can bedata values externally supplied to after the fact analysis; actualnetwork topology; actual unit operating conditions for the day: capacity& ramp limits (data values externally supplied to after the factanalysis); actual Commitment schedules, and the like.

Any changes to the input data associated with specific impact factorsare with respect to the input for reference case dispatch results forafter the fact analysis-High. Hence, even for the same Impact Factor(e.g. reserve requirements), after the fact analysis-Low and after thefact analysis-High may have different input data values.

A relational database, which can be a market database, snapshot can beused with a snapshot capability available for capturing data from thecomprehensive operating plan and other business services (e.g. loadforecast and OI). The snapshot can be time-based or event-based. Theuser can configure the retention policy such as how many days of data tobe retained in the production database. The study case results are alsopersistent in the database for the retention period. As a non-limitingexample, the following data can be stored in the database; loadforecast; regulation reserve requirements; operating reserverequirements; unit megawatt schedules; unit megawatt limits; scheduledoutages; unscheduled outages as detected by the state estimator);transmission security constraints; bus load allocation schedules; themiscellaneous external data associated with actual system operationssuch as tie-line schedules; real-time tie-line schedule adjustments;fuel usage; emission production; real-time ramp rate from externalfunctions, if available, that monitor & management ramp rateperformance; real-time ancillary service requirement for each dispatchinterval from a third party external ancillary service functions and thelike.

In some embodiments, the SKED engine, acting as an optimization engine,can be designed to support comprehensive set of configuration optionsneeded to meet different scheduling requirements. As a non-limitingexample, some functional capabilities of the SKED engine include but arenot limited to: (i) optimized resource commitment and dispatchscheduling with system, operational, and physical constraints; (2)system constraints; (3) energy demand; (4) regulation reserve; (5)operating reserve; (6) transmission security; (7) operationalconstraints; (9) transmission security; (10) energy; (11) minimumup/down times; (12) ramp rate limits; (13) operational bands arounddispatch targets; (14) physical constraints; (15) maximum/minimumenergy; (16) maximum/minimum regulation; (17) price and cost-basedresource model; (18) price for energy and ancillary services; (19) costbased on a fuel, heat rate curve; (20) co-optimization for energy andmultiple ancillary services; (21) constraint relaxation with segmentedweighting of violation quantities; (22) a simultaneous feasibility testcheck can be provided at the end of after the fact analysis study as anoption for network security validation; (23) iterative runs ofsimultaneous feasibility test with any optimization module may not berequired; (24) a mixed integer programming-based optimization enginewith advanced adaptation to scheduling functions; (25) performancemetrics evaluation, and the like.

In some embodiments, the market operator interface case setup can followthe display design of the transmission evaluation application displays.The user can define reference and scenario cases and associationsbetween them. The user is also able to define only one case as thereference case for the day. New display can be provided to facilitateentering of impact factor and scenario data.

In some embodiments, the results can be presented with a combination ofgraphical and tabular displays. Using the information of the batch andperformance metrics defined for the study, the comparison resultsbetween the scenario cases and reference case can be automaticallygenerated and presented in the pre-defined template. Conceptually, afterthe fact analysis results can be presented graphically by a family ofcurves, where each curve can be some performance metric which can beproduction cost, over different dispatch intervals, as a non-limitingexample 48 in a day. This is described below using after the factanalysis-Low in the following example.

Example 6

In this example, curve 1 represents after the fact analysis/with Lowreference dispatch results, that is reference case dispatch results.Curve 2 includes results from scenario #1. Scenario #1 is characterizedby extra regulation requirements to reflect the effects of a specificimpact-factor, namely, tie-line error. Curve 3 illustrates the extracost increase beyond reference case dispatch results. Curve 2 can beattributed to excessive tie-line errors. Curve 3 illustrates the resultsfrom scenario #2: Scenario #2 is characterized by generatorsnot-following dispatch instructions, and can be modeled by additionalregulation requirements beyond reference case dispatch results. Curve 4shows the extra cost increased beyond reference case dispatch resultsthat can be attributed to generator non-compliance. Curve 4 illustratesactual dispatch results from recorded real-time data.

In addition to the after the fact analysis specific displays above,generic case results displays can be used for viewing of after the factanalysis case results.

Because Excel is commonly used by the regional transmission organizationengineers and analyst for comparison of scenario input data, studyresults and statistical analysis, the grid after the fact analysisdisplays in the market operator interface is implemented with thisinteroperability in mind with all market operator Interface grids to beexported to Excel documents. Data can be copied and pasted directlybetween open Excel documents and display grids in the market operatorinterface.

In addition to data supporting data transfer between and Excel, themarket operator interface can also support the export of data from themarket database into existing spreadsheets (i.e. tools and reports) byproviding a special display for the maintenance of mappings betweenstored procedures in the market database (that export data) and targetobjects in Excel.

In some embodiments, an after the fact analysis case for a day can bebased on the day's actual approved production cases. The case filepre-processor can run as an automatic job at non-work hours (e.g., 2am). The files needed for the previous day's after the fact analysiscase can be ready when the after the fact analysis is executed.

In some embodiments, one after the fact analysis interval, including butnot limited to 30 minutes and the like, can cover multiple source caseintervals, including but not limited to 5-minute intervals used by SKED3cases, or 15-minute intervals used by SKED2 cases. Each after the factanalysis interval can use the first Interval data in the interval fileof the source case with the same interval start time as the after thefact analysis interval. As a non-limiting example, an after the factanalysis interval 10:30 (10:30-11:00, assuming interval start timeconvention is used) can use the first interval data of the source casefor interval starting 10:30 (i.e., the 10:30-10:35 Interval for a SKED3case, or the 10:30-10:45 Interval for a SKED2 case). Pre-processorproduces the interval file for after the fact analysis that contains thedata for each after the fact analysis interval and there is one intervalfile produced by the pre-processor for a day. The pre-processor isdesigned to support different intervals, as a non-limiting example, 5,15, 30 minutes and the like, by updating configuration settings and canbe, as a non-limiting example, tested mainly with a 30 minute interval.

In some embodiments, schema structures are built to store historicaldata used by after the fact analysis as Illustrated in FIG. 20. Thisinformation can be exported to the market clearing engine for runningafter the fact analysis cases. For performance and maintenance purposes,these new structures may be implemented in a separate schema from themarket database.

In some embodiments, new market database schema objects are added tomodel impact factors which are input to the respective scenario cases.Impact factors can be perturbation to the input data of the case asillustrated in FIG. 21. Technically the impact factor structures can bevery much akin to some of the existing case override structures in themarket database. However, the new impact factor structures will providemore comprehensive perturbation capability than the existing caseoverride structures. Inside the market clearing engine, the impactfactors can be applied over the case overrides of the production cases.

In some embodiments, after the fact analysis market clearing engine canbe based on a SKED engine. The function of case file and delta filereading is included. In addition to case files of the study intervals,after the fact analysis market clearing engine reads in actual systemoperation data. The major actual data items include: actual load; actualresource output; actual resource ancillary services dispatch; actualresource schedule; actual interchange/transaction and the like.

A market clearing engine may also read in impact factor data as neededfor each scenario. This can include: load change; reserve requirementchange; resource commitment change; resource energy dispatch megawatt orrange change; resource ancillary services dispatch megawatt or rangechange; transaction megawatt change; security constraint change:enforcement/limit, and the like.

In some embodiments, after the fact analysis solution is based on twoimportant assumptions: resources can be controlled to 100% of thefacility rating; and all dispatchable generators will perform at 100% oftheir bid-in ramp rate.

In some embodiments, two-round execution logic is used, where the firstround is for SCUC, based on which, the second round is to do sequentialSCED by interval. In resource dispatch solving, interval t can be basedon the solution of previous interval, i.e., interval t−1. The firstinterval can be based on state estimator. The two intervals can be boundby ramp rate limits. The mixed integer programming method can be used tosolve the first round unit commitment model, the linear programming forthe second round economic dispatch problem.

In some embodiments, in both SCUC and SCED, commitment and dispatch forenergy and ancillary services dispatch can be handled in aco-optimization way. The following models can be included: power balancemodel; resource model; price model; cost-based model; dispatch range;ramp rate limits; maximum/minimum energy limits; commitment eligibility;minimum-up/minimum-down limits; maximum start up limits; ancillaryservices model, which includes ancillary services eligibility, ancillaryservices capacity, and regional requirements; transaction model;security constraint model, which supports multiple penalty segments;security constraint relaxation, where constraint relaxation logic can beincluded to avoid locational marginal price being contaminated bypenalty price in the situation of constraint violations, and the like.

In some embodiments, market clearing engine provides files with powerinjections based on the dispatch solution, which allows simultaneousfeasibility test checking for network security validation. In additionto existing resource commitment/dispatch and security constraintsolutions, bid production costs of both actual operation solution andafter the fact analysis solution can be calculated.

An after the fact analysis market clearing engine reads in input datafor each study interval. The files can be extracted from productioncases executed in study day, which can be usually the previous day orpart of previous day for after the fact analysis by Preprocessor. Foreach after the fact analysis case, the input files can include: onedaily file, which can be the daily file of any production case executedin the study day; one interval file, which can be aggregated based onthe case interval files executed in the study day, and the like.

In some embodiments, the market operator interface provides a userinterface of the following functions: collecting user input dataincluding case parameters and impact factors; displaying summary resultof performance metric; automating data population from database topredefined excel workbook which has data sheets and report sheets;creating report sheets in the excel workbook can be outside the scope ofdeveloping, and the like.

The after the fact analysis case control display looks similar to othercase control display that supports case parameter inputs and; displaycase workflow status and messages.

The read-only display of after the fact analysis case actual values(scenario zero) can display actual values for each after the factanalysis case, illustrated in FIG. 22. This set of values act as thebase for each after the fact analysis case. User friendly filters are onthe left panel to filter the result returned.

The impact factor input display supports inserting, updating anddeleting different impact factor delta values for scenarios as shown inFIG. 23. It supports resource, resource ancillary services, constraints,area, area ancillary services, system and transaction impact factors,and the like. Impact factors can be copied and pasted. Rows can beselected from one grid and copied and pasted on other same type of grid.Case Id and effective and termination times can be adjusted on theselected case.

A list of read-only summary displays for s solutions can be provided,including but are not limited to: performance summary displays forapproved cases; saving summary display to show top ten saving units andsaving by types, and the like. CSV files, with comma-separated values,are used for the digital storage of data structured in a table of listsform, where each associated item, member in a group is in associationwith others also separated by the commas of its set used for the digitalstorage of data structured in a table of lists form. In someembodiments, case comparison can be performed to compare two CSV filesbased on two cases. FIG. 24 illustrates the market operator interfacesolution displays. FIG. 25 illustrates a saving summary display to showthe top ten saving units and saving by types. FIGS. 26 and 27 illustratecase comparison can be performed to compare two CSV files based on twocases.

As a non-limiting example, after the fact analysis can use Excel forreporting purposes. The market operator interface plays a role ofdefining the excel workbook, worksheet and data source mapping. A usercan use the market operator interface to create report mappings andexport data to Excel. The report mapping definition can be a file basedachieve that can be part of installation and can also be defined byusers during runtime. The report mapping definition files can beimported by users and pre-imported by the market operator interface. Thedata source name can be the name of stored procedure that returns reportresult data. The mapping defines the Excel workbook and a particularworksheet. The report query parameters are dependent on database query,stored procedure parameters. The data values can be user defined valuessuch as case id, date and areas. The data values can be predefined keywords such as current, latest, all and the like. Report mappings gridprovides right click context menu run exports, illustrated in FIG. 28.

In some embodiments, creating and editing mappings requires a three-stepwizard: (i) report mappings grid provides right click context menu toadd, edit mapping, where available reports can be driven from databasereport stored procedures, while worksheet names can be driven by aselected Excel file; (ii) set query procedure parameters stored in adatabase, FIG. 29, and the like, where values can be textbox, date anddecimal input controls that map, data and number in the queryparameters; (iii) and the last step is to save the mapping or/and runexport, see FIGS. 30 and 31.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the appended claims.

What is claimed is:
 1. A system, comprising: a memory that storesexecutable instructions; and a processor, coupled to the memory, thatexecutes or facilitates execution of the executable instructions to atleast: integrate at least two load forecasts associated with a powergrid center into a composite load forecast for the power grid center;generate scheduling data for a device of the power grid center based onthe composite load forecast, other scheduling data for the power gridcenter that is determined by a first forecast engine associated with theat least two load forecasts, timing data for at least one powergenerator of the power grid center that is determined by a secondforecast engine associated with the at least two load forecasts, andfinancial data for the power grid center that is determined by a thirdforecast engine associated with the at least two load forecasts, whereinthe first forecast engine is associated with a first time range, thesecond forecast engine is associated with a second time range, and thethird forecast engine is associated with a third time range; and managepower provided to the device of the power grid center based on thescheduling data.
 2. The system of claim 1, wherein the at least two loadforecasts are integrated into the composite load forecast based on aweighted average of the at least two load forecasts.
 3. The system ofclaim 1, wherein a first portion of the first time range corresponds toa second portion of the second time range.
 4. The system of claim 1,wherein the processor further executes or facilitates the execution ofthe executable instructions to: determine the at least two loadforecasts based on the first forecast engine, the second forecast engineand the third forecast engine.
 5. The system of claim 1, wherein theprocessor further executes or facilitates the execution of theexecutable instructions to: generate an operating plan for the powergrid center based on the scheduling data.
 6. The system of claim 1,wherein the processor further executes or facilitates the execution ofthe executable instructions to: modify the scheduling data based on anoverride of data associated with the first forecast engine, the secondforecast engine or the third forecast engine.
 7. The system of claim 1,wherein the processor further executes or facilitates the execution ofthe executable instructions to: display the scheduling data via a userinterface.
 8. A computer readable storage device comprisingcomputer-executable instructions that, in response to execution, causean apparatus comprising a processor, to perform operations, comprising:combining a first load forecast associated with a power grid center anda second load forecast associated with the power grid center into acombined load forecast for the power grid center; generating operatingdata for a device of the power grid center based on the combined loadforecast, scheduling data for the power grid center that is determinedby a first forecast engine associated with a first interval of time,timing data for at least one power generator of the power grid centerthat is determined by a second forecast engine associated with a secondinterval of time, and financial data for the power grid center that isdetermined by a third forecast engine associated with a third intervalof time; and managing power provided to the device of the power gridcenter based on the operating data.
 9. The computer readable storagedevice of claim 8, wherein the combining comprises combining the firstload forecast and the second load forecast based on a weighted averageof the first load forecast and the second load forecast.
 10. Thecomputer readable storage device of claim 8, wherein the generating theoperating data comprises generating the operating data based on a linearinterpolation of the combined load forecast, the scheduling data, thetiming data and the financial data.
 11. The computer readable storagedevice of claim 8, wherein the operations further comprise: receivingthe scheduling data from the first forecast engine, receiving the timingdata from the second forecast engine, and receiving the financial datafrom the third forecast engine.
 12. The computer readable storage deviceof claim 8, wherein the operations further comprise: altering theoperating data based on an override of data associated with the firstforecast engine, the second forecast engine or the third forecastengine.
 13. The computer readable storage device of claim 8, wherein theoperations further comprise: presenting the operating data on aninterface of a user device.
 14. The computer readable storage device ofclaim 8, wherein the operations further comprise: providing theoperating data to a client device.
 15. A method, comprising: merging, bya system comprising a processor, a plurality of load forecastsassociated with a power grid center into a load forecast for the powergrid center; generating, by the system, scheduling data for a device ofthe power grid center based on the load forecast, daily scheduling datafor the power grid center that is determined by a first forecast engineassociated with a first time period and the plurality of load forecasts,timing data for at least one power generator of the power grid centerthat is determined by a second forecast engine associated with a secondtime period and the plurality of load forecasts, and financial data forthe power grid center that is determined by a third forecast engineassociated with a third time period and the plurality of load forecasts;and managing, by the system, an amount of energy provided to the deviceof the power grid center during a period of time based on the schedulingdata.
 16. The method of claim 15, wherein the merging comprises mergingthe plurality of load forecasts based on a weighted average of theplurality of load forecasts.
 17. The method of claim 15, furthercomprising: adjusting, by the system, the scheduling data based on anoverride of data associated with the first forecast engine, the secondforecast engine or the third forecast engine.
 18. The method of claim15, further comprising: generating, by the system, an operating plan forthe device of the power grid center based on the scheduling data. 19.The method of claim 15, further comprising: displaying, by the system,the scheduling data on an interface of a user device.
 20. The method ofclaim 15, further comprising: transmitting, by the system, data to aclient device in accordance with the scheduling data.